To quantitatively understand the cell behavior in molecular level, scientists have developed technologies including high throughput sequencing and flow cytometry. High throughput sequencing can obtain the entire genome sequence and measure expression of large number of genes. Flow cytometry can measure multiple parameters of large number of cells. Both technologies generate large amount of data in high dimension. Therefore, efficient methods to analyze and interpret the data become in demand. In my thesis, I focus on developing computational methods that deliver intuitive and interpretable visualization of biological data. The first chapter describes a software named Cluster-to-Gate (C2G) that can visualize existing clustering results of flow/mass cytometry data in the format of 2D gating hierarchy. Though C2G presents a way to visualize and interpret clustering results, the visualization is still data-driven and no human-knowledge is incorporated. To overcome the limitation of C2G, the second chapter describes a framework that can learn gating approach from existing publications to build a knowledge-graph. This knowledge-graph can automatically suggest order of marker usage and gating hierarchy for new data set, which can be used to gate cell populations. The obtained cell populations are immediately matched to known cell types in the knowledge-graph, which makes them interpretable. The third chapter describe a novel algorithm (GLaMST) to reconstruct lineage tree of B cell receptor gene from high throughput sequencing data. This algorithm outperforms state-of-art in both accuracy and speed.